{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dropout\n",
    "Dropout[1]是一种神经网络正则化的技术，它通过在前向传递中将一些特征随机设置为0来实现。在这个练习中，您将实现一个dropout层，并修改您的全连接网络，以选择使用dropout。\n",
    "\n",
    "[1] Geoffrey E. Hinton et al, \"Improving neural networks by preventing co-adaptation of feature detectors\", arXiv 2012"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train:  (49000, 3, 32, 32)\n",
      "y_train:  (49000,)\n",
      "X_val:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "  print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dropout forward pass\n",
    "在文件`cs231n/layers.py`，为dropout执行向前传递。由于dropout在训练和测试期间的行为不同，请确保实现两种模式的操作。\n",
    "完成之后，运行下面的单元以测试您的实现。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running tests with p =  0.3\n",
      "Mean of input:  10.000207878477502\n",
      "Mean of train-time output:  10.035072797050494\n",
      "Mean of test-time output:  10.000207878477502\n",
      "Fraction of train-time output set to zero:  0.699124\n",
      "Fraction of test-time output set to zero:  0.0\n",
      "\n",
      "Running tests with p =  0.6\n",
      "Mean of input:  10.000207878477502\n",
      "Mean of train-time output:  9.976910758765856\n",
      "Mean of test-time output:  10.000207878477502\n",
      "Fraction of train-time output set to zero:  0.401368\n",
      "Fraction of test-time output set to zero:  0.0\n",
      "\n",
      "Running tests with p =  0.75\n",
      "Mean of input:  10.000207878477502\n",
      "Mean of train-time output:  9.993068588261146\n",
      "Mean of test-time output:  10.000207878477502\n",
      "Fraction of train-time output set to zero:  0.250496\n",
      "Fraction of test-time output set to zero:  0.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(500, 500) + 10\n",
    "\n",
    "for p in [0.3, 0.6, 0.75]:\n",
    "  out, _ = dropout_forward(x, {'mode': 'train', 'p': p})\n",
    "  out_test, _ = dropout_forward(x, {'mode': 'test', 'p': p})\n",
    "\n",
    "  print('Running tests with p = ', p)\n",
    "  print('Mean of input: ', x.mean())\n",
    "  print('Mean of train-time output: ', out.mean())\n",
    "  print('Mean of test-time output: ', out_test.mean())\n",
    "  print('Fraction of train-time output set to zero: ', (out == 0).mean())\n",
    "  print('Fraction of test-time output set to zero: ', (out_test == 0).mean())\n",
    "  print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dropout backward pass\n",
    "在文件`cs231n/layers.py`，实现dropout的向后传递。这样做之后，运行以下单元格来进行数值梯度检查。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx relative error:  5.445612718272284e-11\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 10) + 10\n",
    "dout = np.random.randn(*x.shape)\n",
    "\n",
    "dropout_param = {'mode': 'train', 'p': 0.8, 'seed': 123}\n",
    "out, cache = dropout_forward(x, dropout_param)\n",
    "dx = dropout_backward(dout, cache)\n",
    "dx_num = eval_numerical_gradient_array(lambda xx: dropout_forward(xx, dropout_param)[0], x, dout)\n",
    "\n",
    "print('dx relative error: ', rel_error(dx, dx_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fully-connected nets with Dropout\n",
    "在文件`cs231n/classifiers/fc_net`中。修改您的实现以使用dropout。从表面上看，如果网络的构造函数为“dropout”参数接收到一个非零值，那么网络应该在每个ReLU非线性之后立即添加dropout。这样做之后，运行下面的代码来对实现进行数值梯度检查。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with dropout =  0\n",
      "Initial loss:  2.3004790897684924\n",
      "W1 relative error: 1.48e-07\n",
      "W2 relative error: 2.21e-05\n",
      "W3 relative error: 3.53e-07\n",
      "b1 relative error: 5.38e-09\n",
      "b2 relative error: 2.09e-09\n",
      "b3 relative error: 5.80e-11\n",
      "\n",
      "Running check with dropout =  0.25\n",
      "Initial loss:  2.2924325088330475\n",
      "W1 relative error: 2.74e-08\n",
      "W2 relative error: 2.98e-09\n",
      "W3 relative error: 4.29e-09\n",
      "b1 relative error: 7.78e-10\n",
      "b2 relative error: 3.36e-10\n",
      "b3 relative error: 1.65e-10\n",
      "\n",
      "Running check with dropout =  0.5\n",
      "Initial loss:  2.3042759220785896\n",
      "W1 relative error: 3.11e-07\n",
      "W2 relative error: 1.84e-08\n",
      "W3 relative error: 5.35e-08\n",
      "b1 relative error: 2.58e-08\n",
      "b2 relative error: 2.99e-09\n",
      "b3 relative error: 1.13e-10\n",
      "\n",
      "Running check with dropout =  0.75\n",
      "Initial loss:  2.302848733153321\n",
      "W1 relative error: 7.87e-07\n",
      "W2 relative error: 1.45e-07\n",
      "W3 relative error: 1.70e-07\n",
      "b1 relative error: 1.83e-08\n",
      "b2 relative error: 2.81e-09\n",
      "b3 relative error: 1.49e-10\n",
      "\n",
      "Running check with dropout =  1\n",
      "Initial loss:  2.30485979047391\n",
      "W1 relative error: 8.77e-07\n",
      "W2 relative error: 3.64e-07\n",
      "W3 relative error: 6.33e-08\n",
      "b1 relative error: 1.22e-07\n",
      "b2 relative error: 4.50e-09\n",
      "b3 relative error: 1.15e-10\n",
      "\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for dropout in [0, 0.25, 0.5, 0.75, 1]:\n",
    "  print('Running check with dropout = ', dropout)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            weight_scale=5e-2, dtype=np.float64,\n",
    "                            dropout=dropout, seed=123)\n",
    "  loss, grads = model.loss(X, y)\n",
    "  \n",
    "  print('Initial loss: ', loss)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n",
    "  print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 正则化实验\n",
    "作为一个实验，我们将在500个训练示例上训练网络,然后，我们将在一段时间内可视化这几个网络的训练和验证准确性。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "(Iteration 1 / 125) loss: 7.856643\n",
      "(Epoch 0 / 25) train acc: 0.236000; val_acc: 0.190000\n",
      "(Epoch 1 / 25) train acc: 0.250000; val_acc: 0.178000\n",
      "(Epoch 2 / 25) train acc: 0.360000; val_acc: 0.217000\n",
      "(Epoch 3 / 25) train acc: 0.508000; val_acc: 0.242000\n",
      "(Epoch 4 / 25) train acc: 0.532000; val_acc: 0.242000\n",
      "(Epoch 5 / 25) train acc: 0.544000; val_acc: 0.272000\n",
      "(Epoch 6 / 25) train acc: 0.590000; val_acc: 0.271000\n",
      "(Epoch 7 / 25) train acc: 0.656000; val_acc: 0.252000\n",
      "(Epoch 8 / 25) train acc: 0.750000; val_acc: 0.264000\n",
      "(Epoch 9 / 25) train acc: 0.768000; val_acc: 0.293000\n",
      "(Epoch 10 / 25) train acc: 0.860000; val_acc: 0.316000\n",
      "(Epoch 11 / 25) train acc: 0.858000; val_acc: 0.283000\n",
      "(Epoch 12 / 25) train acc: 0.908000; val_acc: 0.275000\n",
      "(Epoch 13 / 25) train acc: 0.910000; val_acc: 0.305000\n",
      "(Epoch 14 / 25) train acc: 0.952000; val_acc: 0.303000\n",
      "(Epoch 15 / 25) train acc: 0.952000; val_acc: 0.317000\n",
      "(Epoch 16 / 25) train acc: 0.966000; val_acc: 0.308000\n",
      "(Epoch 17 / 25) train acc: 0.958000; val_acc: 0.312000\n",
      "(Epoch 18 / 25) train acc: 0.974000; val_acc: 0.315000\n",
      "(Epoch 19 / 25) train acc: 0.984000; val_acc: 0.306000\n",
      "(Epoch 20 / 25) train acc: 0.972000; val_acc: 0.316000\n",
      "(Iteration 101 / 125) loss: 0.430304\n",
      "(Epoch 21 / 25) train acc: 0.988000; val_acc: 0.327000\n"
     ]
    }
   ],
   "source": [
    "# Train two identical nets, one with dropout and one without\n",
    "np.random.seed(231)\n",
    "num_train = 500\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "solvers = {}\n",
    "dropout_choices = [0, 0.75]\n",
    "for dropout in dropout_choices:\n",
    "  model = FullyConnectedNet([500], dropout=dropout)\n",
    "  print(dropout)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=25, batch_size=100,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 5e-4,\n",
    "                  },\n",
    "                  verbose=True, print_every=100)\n",
    "  solver.train()\n",
    "  solvers[dropout] = solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot train and validation accuracies of the two models\n",
    "\n",
    "train_accs = []\n",
    "val_accs = []\n",
    "for dropout in dropout_choices:\n",
    "  solver = solvers[dropout]\n",
    "  train_accs.append(solver.train_acc_history[-1])\n",
    "  val_accs.append(solver.val_acc_history[-1])\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "for dropout in dropout_choices:\n",
    "  plt.plot(solvers[dropout].train_acc_history, '-o', label='%.2f dropout' % dropout)\n",
    "plt.title('Train accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "  \n",
    "plt.subplot(3, 1, 2)\n",
    "for dropout in dropout_choices:\n",
    "  plt.plot(solvers[dropout].val_acc_history, '-o', label='%.2f dropout' % dropout)\n",
    "plt.title('Val accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question\n",
    "解释你在这个实验中看到的。这说明了什么?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Answer\n",
    "有效的防止了过拟合，"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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